In digital video systems, such as a video camera, a video recorder, a digital camcorder, a set-top digital cable television box, a direct broadcast satellite (DBS) television, a terrestrial digital television, a digital videodisc player (DVDP), a conversational television, a video on demand (VOD), and a video network server, an effective image compression is important. Video contains a continuous amount of data representing moving pictures. The amount of data needed to transfer pictures is high compared with many other types of media.
A video sequence consists of a series of still images or frames. Video compression methods are based on reducing the redundancy and perceptually irrelevant parts of video sequences. The redundancy in video sequences can be categorized into spatial, temporal, and spectral redundancy.
Spatial redundancy means the correlation between neighboring pixels within a frame. Temporal redundancy means the correlation between areas of successive frames. Temporal redundancy arises from the likelihood of objects appearing in a previous image also appearing in the current image. Compression can be achieved by generating motion compensation data, which describes the motion (i.e. displacement) between similar areas of the current and a previous image. The current image is thus predicted from the previous one. Spectral redundancy means the correlation between the different color components of the same image.
Video compression methods typically differentiate between images, which do or do not utilize temporal redundancy reduction. Compressed images which do not utilize temporal redundancy reduction methods are usually called INTRA or I-frames whereas temporally predicted images are called INTER or P-frames (and also B-frames when the INTER frames may be predicted in a forward or backward manner). In the INTER frame case, the predicted (motion-compensated) image is rarely precise enough and therefore a spatially compressed prediction error image is a part of each INTER frame.
A video includes a series of frames taken over time. For example, a sensor can be used that captures an image. The image can be saved as one of a series of frames in some form of memory. By taking a series of frames over time, such as 60 frames per second, a video may be formed that can be watched by a user. In order for the sensor to function, typically, the sensor is mounted in housing and an optics module is used to focus the desired image in the focal plane on the sensor so that the image can be processed and stored. The housing, optics module and the sensor are part of the platform and the overall system is familiar to a person of skill in the art.
Thus, a need still remains for a video system with quantization matrix for increasing levels of functionality. In view of ease of use, it is increasingly critical that answers be found to these problems. In view of the ever-increasing commercial competitive pressures, along with growing consumer expectations and the diminishing opportunities for meaningful product differentiation in the marketplace, it is critical that answers be found for these problems. Additionally, the need to reduce costs, improve efficiencies and performance, and meet competitive pressures adds an even greater urgency to the critical necessity for finding answers to these problems.
Solutions to these problems have been long sought but prior developments have not taught or suggested any solutions and, thus, solutions to these problems have long eluded those skilled in the art.